(II) Pooling layer: The feature maps generated from the convolutional operations are sub-sampled in the pooling layer. This reduces the large size feature maps to generate smaller feature maps. The pooling layer reduces the dimension of the feature map resulting in reduction in the number of parameters to learn. It also reduces the computation that needs to be performed. There are various types of pooling such as average pooling, max pooling, min pooling, global average pooling (GAP) etc. It may be possible sometimes that the performance output of CNN model decreases because of the pooling layer as it focuses primarily on ascertaining the correct location of a feature rather than focusing on particular features available in the data[1]}, [2]}, [3]}.
